mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-02 04:31:25 +00:00
86 lines
3.5 KiB
C++
86 lines
3.5 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include "ck_tile/core.hpp"
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#include "ck_tile/host/kernel_launch.hpp"
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#include "ck_tile/ops/epilogue.hpp"
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#include "ck_tile/ops/gemm.hpp"
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struct GemmConfigurationBase
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{
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static constexpr bool PAD_M = true;
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static constexpr bool PAD_N = true;
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static constexpr bool PAD_K = true;
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static constexpr bool PERMUTE_A = false;
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static constexpr bool PERMUTE_B = false;
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static constexpr bool TRANSPOSE_C = false;
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static constexpr bool USE_STRUCTURED_SPARSITY = false;
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static constexpr int BLOCK_PER_CU = 1;
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static constexpr auto SCHEDULER = ck_tile::GemmPipelineScheduler::Intrawave;
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static constexpr ck_tile::index_t NUM_WAVE_GROUPS = 1;
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static constexpr bool PRESHUFFLE = false;
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static constexpr bool DOUBLE_SMEM_BUFFER = false;
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};
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template <typename PrecisionType, bool IsPersistent>
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struct GemmConfigurationMemoryInterwave : public GemmConfigurationBase
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{
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static constexpr ck_tile::index_t M_TILE = 256;
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static constexpr ck_tile::index_t N_TILE = 256;
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static constexpr ck_tile::index_t K_TILE = 16;
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static constexpr ck_tile::index_t M_WARP = 2;
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static constexpr ck_tile::index_t N_WARP = 2;
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static constexpr ck_tile::index_t K_WARP = 1;
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static constexpr ck_tile::index_t M_WARP_TILE = 32;
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static constexpr ck_tile::index_t N_WARP_TILE = 32;
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static constexpr ck_tile::index_t K_WARP_TILE = sizeof(PrecisionType) == 2 ? 8 : 16;
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static constexpr bool PERSISTENT = IsPersistent;
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static constexpr auto SCHEDULER = ck_tile::GemmPipelineScheduler::Intrawave;
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};
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template <typename ADataType_, typename BDataType_ = ADataType_, typename CDataType_ = ADataType_>
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struct StreamKGemmTypeConfiguration
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{
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using ADataType = ADataType_;
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using BDataType = BDataType_;
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using AccDataType = float;
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using CDataType = CDataType_;
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};
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auto createArgs(int argc, char* argv[])
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{
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ck_tile::ArgParser arg_parser;
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arg_parser.insert("m", "512", "m dimension")
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.insert("n", "512", "n dimension")
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.insert("k", "512", "k dimension")
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.insert("a_layout", "R", "A tensor data layout - Row by default")
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.insert("b_layout", "C", "B tensor data layout - Column by default")
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.insert("c_layout", "R", "C tensor data layout - Row by default")
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.insert("reduction_strategy",
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"atomic",
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"strategy for storing results in C tensor - atomic/reduction")
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.insert("persistent_dp",
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"0",
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"0. Non-persistent data-parallel section, 1 Fully persistent kernel.")
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.insert("stride_a", "0", "Tensor A stride")
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.insert("stride_b", "0", "Tensor B stride")
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.insert("stride_c", "0", "Tensor C stride")
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.insert("v", "2", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
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.insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8")
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.insert("warmup", "50", "number of iterations before benchmarking the kernel")
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.insert("repeat", "100", "number of iterations to benchmark the kernel")
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.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
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.insert("init", "0", "0:random, 1:linear, 2:constant(1)")
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.insert("flush_cache", "true", "flush cache before running the kernel, defaults to true");
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bool result = arg_parser.parse(argc, argv);
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return std::make_tuple(result, arg_parser);
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}
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